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This repo contains code for training expert trajectories and distilling synthetic data from our Dataset Distillation by FTD paper (CVPR 2023).
The main features of angusdujw/ftd-distillation are: Gradient Trajectory Matching.
Projects with overlapping indexed features include: gzyaftermath/datm — Code. justincui03/tesla — Hello!!! Thanks for checking out our repo and paper! 🍻. nialiu/att — This repository contains code for training expert trajectories and distilling synthetic data for the paper: Dataset… nus-hpc-ai-lab/edf — In this work, we propose to emphasize discriminative features for dataset distillation in the complex scenario, i.e.… nus-hpc-ai-lab/pad — Matching-based Dataset Distillation methods can be summarized into two steps:. georgecazenavette/mtt-distillation — This repo contains code for training expert trajectories and distilling synthetic data from our Dataset Distillation…
This repository contains code for training expert trajectories and distilling synthetic data for the paper: Dataset Distillation by Automatic Training Trajectories. The listed is the steps to run the code. 1. Set up enveriments. 2. Create an wandb account for monitoring distillation process…
This repo contains code for training expert trajectories and distilling synthetic data from our Dataset Distillation by Matching Training Trajectories paper (CVPR 2022). Please see our project page for more results.
